Several fusion techniques such as averaging, majority voting [1]}, bagging [2]}, stacking [3]}, random forests [4]}, error correcting output coding [5]}, [6]} and their variants have been widely used in traditional machine learning. However, extensions of some of these approaches to deep learning (DL) systems
have been deemed inefficient and challenging, due to the computational complexity associated with the training of deep networks, as well as the difficulty in securing diversity among the base classifiers.
Therefore, most of the state-of-art DL ensembles are either formed of simple averaging (or voting) frameworks comprising only a small number of
base classifiers [7]}, [8]}, [9]}, [10]}, [11]}, or weak decision tree ensembles based on boosting deep features that have been already extracted [12]}, [13]}, [14]}, [15]}.
